





'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class 		lowerCamelCase	(   _A							):
	snake_case_   = 42






class 		lowerCamelCase	(   _A   , _A							):



	@register_to_config
	def __init__( self		,   a_ = 65_536		,   a_ = None		,   a_ = 2		,   a_ = 2		,   a_ = 0		,   a_ = "fourier"		,   a_ = True		,   a_ = False		,   a_ = 0.0		,   a_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,   a_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,   a_ = "UNetMidBlock1D"		,   a_ = None		,   a_ = (32, 32, 64)		,   a_ = None		,   a_ = 8		,   a_ = 1		,   a_ = False		,   ):
			super().__init__()
			lowerCAmelCase  :     Optional[Any]       						= sample_size

			# time
			if time_embedding_type == "fourier":
					lowerCAmelCase  :     Optional[Any]       						= GaussianFourierProjection(
					    embedding_size=8		,   set_W_to_weight=a_		,   log=a_		,   flip_sin_to_cos=a_						)
					lowerCAmelCase  :     Tuple       						= 2 * block_out_channels[0]
			elif time_embedding_type == "positional":
					lowerCAmelCase  :     List[str]       						= Timesteps(
					    block_out_channels[0]		,   flip_sin_to_cos=a_		,   downscale_freq_shift=a_						)
					lowerCAmelCase  :     List[Any]       						= block_out_channels[0]

			if use_timestep_embedding:
					lowerCAmelCase  :     Union[str, Any]       						= block_out_channels[0] * 4
					lowerCAmelCase  :     int       						= TimestepEmbedding(
					    in_channels=a_		,   time_embed_dim=a_		,   act_fn=a_		,   out_dim=block_out_channels[0]		,   )

			lowerCAmelCase  :     Any       						= nn.ModuleList([]						)
			lowerCAmelCase  :     Any       						= None
			lowerCAmelCase  :     int       						= nn.ModuleList([]						)
			lowerCAmelCase  :     Dict       						= None

			# down
			lowerCAmelCase  :     Optional[int]       						= in_channels
			for i, down_block_type in enumerate(a_						):
					lowerCAmelCase  :     Optional[Any]       						= output_channel
					lowerCAmelCase  :     Dict       						= block_out_channels[i]

					if i == 0:
							input_channel += extra_in_channels

					lowerCAmelCase  :     Tuple       						= i == len(a_						) - 1

					lowerCAmelCase  :     Any       						= get_down_block(
					    a_		,   num_layers=a_		,   in_channels=a_		,   out_channels=a_		,   temb_channels=block_out_channels[0]		,   add_downsample=not is_final_block or downsample_each_block		,   )
					self.down_blocks.append(a_						)

			# mid
			lowerCAmelCase  :     Union[str, Any]       						= get_mid_block(
			    a_		,   in_channels=block_out_channels[-1]		,   mid_channels=block_out_channels[-1]		,   out_channels=block_out_channels[-1]		,   embed_dim=block_out_channels[0]		,   num_layers=a_		,   add_downsample=a_		,   )

			# up
			lowerCAmelCase  :     Optional[int]       						= list(reversed(a_						)						)
			lowerCAmelCase  :     Tuple       						= reversed_block_out_channels[0]
			if out_block_type is None:
					lowerCAmelCase  :     List[str]       						= out_channels
			else:
					lowerCAmelCase  :     Union[str, Any]       						= block_out_channels[0]

			for i, up_block_type in enumerate(a_						):
					lowerCAmelCase  :     Optional[int]       						= output_channel
					lowerCAmelCase  :     Dict       						= (
					    reversed_block_out_channels[i + 1] if i < len(a_						) - 1 else final_upsample_channels
					)

					lowerCAmelCase  :     Dict       						= i == len(a_						) - 1

					lowerCAmelCase  :     List[str]       						= get_up_block(
					    a_		,   num_layers=a_		,   in_channels=a_		,   out_channels=a_		,   temb_channels=block_out_channels[0]		,   add_upsample=not is_final_block		,   )
					self.up_blocks.append(a_						)
					lowerCAmelCase  :     Any       						= output_channel

			# out
			lowerCAmelCase  :     Union[str, Any]       						= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,   32						)
			lowerCAmelCase  :     Tuple       						= get_out_block(
			    out_block_type=a_		,   num_groups_out=a_		,   embed_dim=block_out_channels[0]		,   out_channels=a_		,   act_fn=a_		,   fc_dim=block_out_channels[-1] // 4		,   )





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = True		,   ):
			lowerCAmelCase  :     List[Any]       						= timestep
			if not torch.is_tensor(a_						):
					lowerCAmelCase  :     str       						= torch.tensor([timesteps]		,   dtype=torch.long		,   device=sample.device						)
			elif torch.is_tensor(a_						) and len(timesteps.shape						) == 0:
					lowerCAmelCase  :     Optional[Any]       						= timesteps[None].to(sample.device						)

			lowerCAmelCase  :     Dict       						= self.time_proj(a_						)
			if self.config.use_timestep_embedding:
					lowerCAmelCase  :     Optional[Any]       						= self.time_mlp(a_						)
			else:
					lowerCAmelCase  :     Any       						= timestep_embed[..., None]
					lowerCAmelCase  :     List[str]       						= timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
					lowerCAmelCase  :     Tuple       						= timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

			# 2. down
			lowerCAmelCase  :     Optional[Any]       						= ()
			for downsample_block in self.down_blocks:
					lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= downsample_block(hidden_states=a_		,   temb=a_						)
					down_block_res_samples += res_samples

			# 3. mid
			if self.mid_block:
					lowerCAmelCase  :     List[str]       						= self.mid_block(a_		,   a_						)

			# 4. up
			for i, upsample_block in enumerate(self.up_blocks						):
					lowerCAmelCase  :     List[str]       						= down_block_res_samples[-1:]
					lowerCAmelCase  :     Optional[int]       						= down_block_res_samples[:-1]
					lowerCAmelCase  :     str       						= upsample_block(a_		,   res_hidden_states_tuple=a_		,   temb=a_						)

			# 5. post-process
			if self.out_block:
					lowerCAmelCase  :     Any       						= self.out_block(a_		,   a_						)

			if not return_dict:
					return (sample,)

			return UNetaDOutput(sample=a_						)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase									=   {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



